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SherpaMind#
Use SherpaMind as the OpenClaw query/action layer over the local SherpaDesk dataset prepared by the backend service.
Repo root and stable entrypoint#
Work from the repo root:
bash
cd {baseDir}
When the repo is installed under an OpenClaw skills/ directory, SherpaMind automatically uses the parent workspace as SHERPAMIND_WORKSPACE_ROOT, so runtime state stays in workspace-level .SherpaMind/ rather than inside the skill checkout.
Use the stable runner:
bash
python3 scripts/run.py <command> [args...]
Do not invent alternate runtime paths. Do not treat OpenClaw as the background scheduler for this backend.
Transparency and operator expectations#
SherpaMind is not an instruction-only skill.
When installed and configured for live use, it can:
- create workspace-local runtime state under
.SherpaMind/ - create staged runtime dirs under
.SherpaMind/private/config/,.SherpaMind/private/secrets/,.SherpaMind/private/data/,.SherpaMind/private/state/,.SherpaMind/private/logs/,.SherpaMind/private/runtime/, and.SherpaMind/public/ - create a local SQLite database and generated public artifacts
- create a Python runtime venv under
.SherpaMind/private/runtime/venv - install Python dependencies from PyPI during bootstrap
- store the SherpaDesk API key locally in
.SherpaMind/private/secrets/sherpadesk_api_key.txt - optionally store a SherpaDesk API user hint in
.SherpaMind/private/secrets/sherpadesk_api_user.txt - store non-secret connection/runtime settings in
.SherpaMind/private/config/settings.env - optionally install and run a user-level
systemdbackground service
Required live staged credentials/config for real SherpaDesk use:
- staged API key under
.SherpaMind/private/secrets/sherpadesk_api_key.txt - staged org/instance settings in
.SherpaMind/private/config/settings.env
If the user only wants query guidance or offline inspection of an existing local dataset, do not imply that fresh credentials or service installation are unnecessary for live sync.
Choose the lightest path that answers the question#
Exact facts, counts, status, and workload#
Start with structured commands:
python3 scripts/run.py dataset-summarypython3 scripts/run.py report-api-usagepython3 scripts/run.py report-enrichment-coveragepython3 scripts/run.py insight-snapshotpython3 scripts/run.py report-ticket-countspython3 scripts/run.py report-status-countspython3 scripts/run.py report-priority-countspython3 scripts/run.py report-technician-countspython3 scripts/run.py report-ticket-log-typespython3 scripts/run.py report-attachment-summarypython3 scripts/run.py recent-ticketspython3 scripts/run.py open-ticket-agespython3 scripts/run.py recent-account-activitypython3 scripts/run.py recent-technician-loadpython3 scripts/run.py account-summary "<account>"python3 scripts/run.py technician-summary "<technician>"python3 scripts/run.py ticket-summary "<ticket-id|ticket-number|ticket-key>"
Examples:
- open-ticket count →
report-status-counts - technician backlog/load →
technician-summary "<technician>" - account snapshot →
account-summary "<account>" - ticket inspection / retrieval-ready context →
ticket-summary "<ticket-id|ticket-number|ticket-key>"
Fuzzy investigation, prior-art lookup, and support-history recall#
Use retrieval commands:
python3 scripts/run.py search-ticket-docs "<query>"python3 scripts/run.py search-ticket-docs "<query>" --account "<account>" --status Open --department "<department>"python3 scripts/run.py search-ticket-chunks "<query>"python3 scripts/run.py search-ticket-chunks "<query>" --account "<account>" --status Open --technician "<technician>"python3 scripts/run.py search-ticket-chunks "<query>" --priority High --category "<category>" --class-name "<class>"python3 scripts/run.py search-vector-index "<query>"python3 scripts/run.py search-vector-index "<query>" --account "<account>" --status Openpython3 scripts/run.py search-vector-index "<query>" --technician "<technician>" --priority High --category "<category>"python3 scripts/run.py search-vector-index "<query>" --department "<department>" --class-name "<class>" --submission-category "<channel>" --resolution-category "<resolution>"
Default retrieval workflow:
- Start with keyword/text search when the issue words are concrete.
- Widen to vector search when wording may vary or keyword recall looks thin.
- Use account/technician/status/priority/category/department/class/submission/resolution filters when they materially narrow the search.
- Answer from retrieved evidence instead of jumping to canned conclusions.
Quick factual context from generated artifacts#
Read these when a concise derived artifact is enough:
{baseDir}/.SherpaMind/public/docs/index.md{baseDir}/.SherpaMind/public/docs/insight-snapshot.md{baseDir}/.SherpaMind/public/docs/stale-open-tickets.md{baseDir}/.SherpaMind/public/docs/recent-account-activity.md{baseDir}/.SherpaMind/public/docs/recent-technician-load.md{baseDir}/.SherpaMind/public/docs/runtime/status.md{baseDir}/.SherpaMind/public/docs/accounts/index.md{baseDir}/.SherpaMind/public/docs/technicians/index.md{baseDir}/.SherpaMind/public/docs/tickets/index.md{baseDir}/.SherpaMind/public/docs/accounts/*.md{baseDir}/.SherpaMind/public/docs/technicians/*.md{baseDir}/.SherpaMind/public/docs/tickets/ticket_*.md
Preferred answer flow#
For broad questions like “what’s been going on with account X lately?” or “have we seen this before?”:
- Pull one structural summary first.
- Pull retrieval evidence second.
- Use generated public docs only when they add concise context.
- Give the user an answer grounded in the retrieved evidence.
Prefer factual retrieval over hand-authored interpretation.
End-to-end install and onboarding on another OpenClaw instance#
If the user asks to install SherpaMind properly end-to-end into an OpenClaw instance, first check the host prerequisites and report any missing pieces plainly before continuing.
Minimum prerequisites to check:
python3is present- Python venv/pip bootstrap works on that host
- the host has network access for Python package installation
systemctl --useris available if background service mode is expected
If any prerequisite is missing, stop and tell the user exactly what is missing and what needs to be fixed.
Then use this flow from the installed skill bundle root:
- audit bootstrap/readiness first
python3 scripts/run.py bootstrap-audit
- bootstrap the skill-local runtime
python3 scripts/bootstrap.py
- run the setup flow
python3 scripts/run.py setup
- verify runtime state
python3 scripts/run.py doctor
- stage the SherpaDesk API key
python3 scripts/run.py stage-api-key --from-file <path-to-token-file>
- discover organizations/instances
python3 scripts/run.py discover-orgs
- write the chosen org/instance into non-secret settings
python3 scripts/run.py configure --org-key <org> --instance-key <instance>
- seed the local dataset
python3 scripts/run.py seed
- generate/refine the derived artifacts if needed
python3 scripts/run.py generate-public-snapshotpython3 scripts/run.py generate-runtime-status
- confirm the install is actually usable
python3 scripts/run.py dataset-summarypython3 scripts/run.py insight-snapshotpython3 scripts/run.py report-vector-index-status
- only then decide whether unattended background mode is wanted
python3 scripts/run.py install-servicepython3 scripts/run.py service-status
Default expectation on Linux is that setup initializes the DB, cleans up any old SherpaMind cron jobs, and can generate an initial public snapshot. Treat user-level systemd installation as a later, explicit operator choice rather than part of the earliest bootstrap steps.
If service installation fails because the target host lacks usable systemctl --user, continue the bootstrap/config/seed flow anyway, report the service limitation clearly, and use python3 scripts/run.py service-run-once or python3 scripts/run.py service-run as the fallback operational mode instead of pretending the service installed.
If install/runtime/use issues or meaningful feature gaps are discovered while operating SherpaMind, check https://github.com/kklouzal/SherpaMind/issues. If a matching issue exists, add supporting detail; otherwise open a new issue with clear reproduction/context. Keep issue content anonymized and public-safe.
Lifecycle and maintenance commands#
Use these for setup/maintenance, not routine user queries:
python3 scripts/bootstrap.pypython3 scripts/run.py workspace-layoutpython3 scripts/run.py doctorpython3 scripts/run.py backfill-technician-stubspython3 scripts/run.py backfill-ticket-entity-stubspython3 scripts/run.py bootstrap-auditpython3 scripts/run.py setuppython3 scripts/run.py migrate-legacy-statepython3 scripts/run.py archive-legacy-statepython3 scripts/run.py cleanup-legacy-cronpython3 scripts/run.py stage-api-key --from-file <path-to-token-file>python3 scripts/run.py discover-orgspython3 scripts/run.py configure --org-key <org> --instance-key <instance>python3 scripts/run.py install-servicepython3 scripts/run.py restart-servicepython3 scripts/run.py service-statuspython3 scripts/run.py generate-public-snapshotpython3 scripts/run.py generate-runtime-status
Boundaries#
- Treat SherpaMind as read-only unless the project explicitly grows write behavior later.
- Keep attachment handling metadata-only by default.
- Do not auto-download attachment bodies by default.
- Treat docs, chunks, vector rows, and public Markdown artifacts as replaceable derived caches.
- Let SherpaMind prepare and expose data; let OpenClaw interpret it at answer time.
References#
Read these only when needed. Keep the action layer in this file lean; use the reference files for deeper architecture, retrieval, automation, and API details.
{baseDir}/README.md— current live project overview and command surface{baseDir}/references/openclaw-query-model.md— query/retrieval model{baseDir}/references/architecture-doctrine.md— backend vs skill-front boundary{baseDir}/references/retrieval-architecture.md— retrieval and vector design{baseDir}/references/bootstrap-onboarding.md— audit-first install/onboarding model{baseDir}/references/automation.md— service/install/update model{baseDir}/references/delta-sync-strategy.md— hot/warm/cold sync behavior{baseDir}/references/api-reference.md— verified API/auth behavior
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